An unmanned intelligent compaction method, device, medium, and product

Abstract

The application relates to the technical field of unmanned driving, in particular to an unmanned intelligent compaction method, equipment, medium and product, which comprises the following steps: performing parameter analysis based on compaction area information and road roller information to determine road roller parameters. Then, the spatial position and attitude information of the road roller are acquired in real time, unmanned driving analysis is performed based on the spatial position, attitude information and road roller parameters, a compaction control instruction is generated, and the road roller is controlled to perform compaction work by using the compaction control instruction. Finally, multi-dimensional compaction data are acquired in real time in the process that the road roller performs the compaction work, and the multi-dimensional compaction data are stored into a compaction work database. In the process of setting the road roller parameters, the compaction area information and the road roller information are comprehensively considered, so that the road roller parameters can be highly matched with the compaction material type and compaction material characteristics of the compaction area, the compaction effect of the road roller can be fully exerted, and the stability of the compaction quality is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned driving, and in particular to an unmanned intelligent compaction method, device, medium and product. BACKGROUND

[0002] With the continuous development of modern engineering technology and the wide application of intelligent technology, unmanned technology has shown great application potential and value in various engineering fields. In particular, in the fields of road construction, civil engineering, and mine exploitation, the emergence of unmanned road rollers not only greatly improves construction efficiency, but also reduces the risk and cost of manual operation.

[0003] In recent years, various unmanned road rollers and intelligent compaction systems have appeared at home and abroad. The unmanned road rollers in the related technology can only realize basic unmanned driving functions and lack organic combination with digital compaction quality. However, in actual application, the unmanned road roller is affected by various factors, such as changes in soil properties, differences in compaction equipment performance, and complexity of the construction environment, resulting in fluctuations in the stability of the compaction quality, and the compaction quality stability is poor.

[0004] Therefore, how to improve the stability of the compaction quality is a problem to be solved by those skilled in the art. SUMMARY

[0005] The purpose of the present application is to provide an unmanned intelligent compaction method, device, medium and product to solve at least one of the above technical problems.

[0006] The above invention purpose of the present application is realized by the following technical scheme:

[0007] In a first aspect, the present application provides an unmanned intelligent compaction method, which adopts the following technical scheme:

[0008] An unmanned intelligent compaction method comprises:

[0009] Obtaining compaction area information and road roller information, performing parameter analysis based on the compaction area information and the road roller information, and determining road roller parameters, wherein the road roller parameters include path data, vibration state, and travel speed;

[0010] Real-time acquisition of spatial position and attitude information of the road roller, unmanned driving analysis based on the spatial position, the attitude information and the road roller parameters, generation of compaction control instructions, and control of the road roller for compaction operation by using the compaction control instructions;

[0011] In the process that the road roller performs the compaction work, multi-dimensional compaction data is acquired in real time, and the multi-dimensional compaction data is stored into a compaction work database, wherein the multi-dimensional compaction data comprises real-time driving parameters, real-time environment sensing data, compaction work parameters and device state information.

[0012] By adopting the technical scheme, compaction area information and road roller information are acquired, parameter analysis is performed based on the compaction area information and the road roller information, and road roller parameters are determined. Then, spatial position and attitude information of the road roller is acquired in real time, unmanned driving analysis is performed based on the spatial position, the attitude information and the road roller parameters, compaction control instructions are generated, and the compaction control instructions are used to control the road roller to perform the compaction work. Finally, in the process that the road roller performs the compaction work, multi-dimensional compaction data is acquired in real time, and the multi-dimensional compaction data is stored into a compaction work database. In the process of setting the road roller parameters, the compaction area information and the road roller information are comprehensively considered, so that the road roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area, and the compaction effect can reach the expected effect based on the compaction area information, so that the compaction machine can fully exert its compaction effect, and the fluctuation of the compaction quality is reduced, and the stability of the compaction quality is improved.

[0013] In a preferred example, the application can be further configured to, after the multi-dimensional compaction data is stored into the compaction work database, further comprising:

[0014] When it is detected that one round of compaction work on the compaction area is completed, compaction work whole-process data is extracted from the compaction work database, wherein the compaction work whole-process data comprises a driving track corresponding to the road roller, land environment data corresponding to the driving track, and compaction evaluation indexes corresponding to the driving track.

[0015] Based on the compaction evaluation indexes in the compaction work whole-process data, the compaction area is divided into compaction road sections, and a plurality of compaction road sections are determined.

[0016] For a target compaction road section, based on the land environment data, the compaction evaluation indexes and the road roller information corresponding to the target compaction road section, parameter optimization is performed, and optimized road roller parameters are determined, wherein the target compaction road section is any one of the plurality of compaction road sections.

[0017] In a preferred example, the application can be further configured to, after the multi-dimensional compaction data is acquired in real time in the process that the road roller performs the compaction work, further comprising:

[0018] Based on the real-time environment sensing data, obstacle identification is performed, and obstacle information is determined, wherein the obstacle information comprises an obstacle type, an obstacle position and an obstacle size.

[0019] obstacle information based on the current spatial position, the current attitude information, the roller parameters and the obstacle information, and determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information comprises an obstacle avoidance type and an obstacle avoidance operation.

[0020] In a preferred example, the application can be further configured to, after the autonomous obstacle avoidance planning based on the current spatial position, the current attitude information, the roller parameters and the obstacle information, and determining the autonomous obstacle avoidance information, further comprise:

[0021] When the obstacle avoidance type is trajectory change obstacle avoidance, perform obstacle avoidance control analysis based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, generate trajectory change obstacle avoidance instructions, and send the trajectory change obstacle avoidance instructions to the roller, so that the roller avoids obstacles during the compaction operation;

[0022] When the obstacle avoidance type is emergency braking, perform braking distance calculation based on the roller parameters to determine an emergency braking distance;

[0023] Perform pre-warning feasibility analysis based on the current spatial position, the obstacle position and the emergency braking distance to determine a pre-warning feasibility analysis result;

[0024] When the pre-warning feasibility analysis result is pre-warning feasible, generate an emergency braking pre-warning; and when the pre-warning feasibility analysis result is pre-warning infeasible, control the roller to perform the emergency braking operation in the obstacle avoidance operation, so that the roller avoids obstacles during the compaction operation.

[0025] In a preferred example, the application can be further configured to, the parameter analysis based on the compaction area information and the roller information to determine roller parameters, comprising:

[0026] Perform driving path analysis based on the compaction area size and the compaction area shape in the compaction area information to determine path data;

[0027] Perform vibration analysis based on the compaction material type and the compaction material characteristics in the compaction area information to determine a vibration state;

[0028] Perform travel analysis based on the compaction material type in the compaction area information and the equipment weight in the roller information to determine a travel speed;

[0029] Integrate the path data, the vibration state and the travel speed to determine roller parameters.

[0030] The application can be further configured to, after storing the multi-dimensional compaction data into the compaction operation database, further include:

[0031] Based on the compaction operation database, operation data screening is performed to determine a target compaction operation data set, wherein the target compaction operation data set includes multi-pass road compaction data, and each pass of the road compaction data is multi-dimensional compaction data corresponding to a same compaction area.

[0032] An engineering construction map is obtained, and based on the engineering construction map and the target compaction operation data set, compaction visual display is performed to obtain a compaction visual display map, wherein the compaction visual display map displays each pass of the road compaction data in a separate layer.

[0033] In a second aspect, the application provides an electronic device, which adopts the following technical solution:

[0034] At least one processor;

[0035] A memory;

[0036] At least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the unmanned intelligent compaction method described above.

[0037] In a third aspect, the application provides a computer-readable storage medium, which adopts the following technical solution:

[0038] A computer-readable storage medium having a computer program stored thereon, when the computer program is executed in a computer, the computer is caused to execute the unmanned intelligent compaction method described above.

[0039] In a fourth aspect, the application provides a computer program product, which adopts the following technical solution:

[0040] A computer program product, comprising a computer program, when the computer program is executed by a processor, the unmanned intelligent compaction method described above is realized.

[0041] In summary, the application includes at least one of the following beneficial technical effects:

[0042] The compaction area information and the roller information are acquired, parameter analysis is performed based on the compaction area information and the roller information, and the roller parameters are determined. Then, the spatial position and attitude information of the roller are acquired in real time, unmanned driving analysis is performed based on the spatial position, the attitude information, and the roller parameters, a compaction control instruction is generated, and the roller is controlled to perform the compaction operation by using the compaction control instruction. Finally, in the process of performing the compaction operation by the roller, multi-dimensional compaction data are acquired in real time, and the multi-dimensional compaction data are stored in the compaction operation database. In the process of setting the roller parameters, the compaction area information and the roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area, so that the compaction effect can reach the expected effect based on the compaction area information, the compaction machine can fully play its compaction effect, the fluctuation of the compaction quality is reduced, and the stability of the compaction quality is improved.

[0043] When it is detected that one round of compaction operation is completed for the compaction area, the compaction operation whole-process data are extracted from the compaction operation database, the compaction area is divided into multiple compaction road sections based on the compaction evaluation indexes in the compaction operation whole-process data, and the multiple compaction road sections are determined. After the roller completes one round of compaction operation, in-depth analysis is performed based on the compaction operation whole-process data, so that potential quality problems can be found in time, the working parameters of the roller are accurately adjusted, the compaction effect of subsequent compaction operation can reach the preset requirement, and thus the overall compaction quality is improved. Then, for the target compaction road section, parameter optimization is performed based on the land environment data corresponding to the target compaction road section, the compaction evaluation indexes, and the roller information, the optimized roller parameters are determined, and the optimized roller parameters obtained by performing the parameter optimization are helpful for the roller to achieve the best compaction effect in the next round of compaction operation, improve the compactness and strength of the road surface, and thus improve the overall quality of the road surface. BRIEF DESCRIPTION OF DRAWINGS

[0044] Fig. 1 is a flowchart of a kind of unmanned driving intelligent compaction method of one embodiment of the application;

[0045] Fig. 2 is a structural schematic diagram of a kind of unmanned driving intelligent compaction system of one embodiment of the application;

[0046] Fig. 3 is a structural schematic diagram of a kind of electronic equipment of one embodiment of the application. DETAILED DESCRIPTION

[0047] The following Figs. 1 to 3 The application is further described in detail.

[0048] The specific embodiments are merely explanatory of this application, and are not intended to limit this application. Those skilled in the art can make modifications to the embodiments without creative contribution after reading the specification, and as long as the modifications are within the scope of this application, they are protected by the Patent Law.

[0049] To make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative contribution fall within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved in the embodiments of the present application need to be obtained with the permission or consent of the object when the embodiments of the present application are applied to specific products or technologies, and the collection, use and processing of the related data need to comply with the relevant laws, regulations and standards of the country and region. That is, if the embodiments of the present application involve data related to the object, the data needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant department, and the compliance with the relevant laws, regulations and standards of the country and region. If the embodiments involve personal information, the consent of the individual needs to be obtained for the acquisition of all personal information, and the separate consent of the information subject needs to be obtained for the acquisition of sensitive information, and the embodiments also need to be implemented with the authorization and consent of the object.

[0050] In addition, the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents an "or" relationship between the associated objects unless otherwise specified.

[0051] The embodiments of the present application will be described in further detail below with reference to the drawings of the specification.

[0052] The embodiments of the present application provide an unmanned intelligent compaction method, which is executed by an electronic device, which can be a server or a terminal device. The server can be a physical server, a server cluster composed of multiple physical servers, or a distributed system, and can also be a cloud server providing cloud computing services. As shown in the figure, the method comprises steps S101, S102 and S103. Fig. 1

[0053] ​In step S101, the compaction area information and the roller information are acquired, parameter analysis is performed based on the compaction area information and the roller information, and the roller parameters are determined, wherein the roller parameters include path data, vibration state, and travel speed.

[0054] For the embodiments of the present application, the compaction area information and the roller information for performing the compaction operation are pre-stored in the electronic device, the compaction area information includes but is not limited to the compaction area size, the compaction area shape, the compaction material type (for example, soil, asphalt, cement, etc.), and the compaction material characteristics (for example, the water content, the density, the particle size, etc. of the material); the roller information includes but is not limited to the roller weight, the vibration system performance (for example, the vibration frequency, the amplitude, etc. of the key parameters), and the travel system performance (for example, the travel speed, the steering ability, the climbing ability, etc. of the key parameters). Then, based on the compaction area information and the roller information, parameter analysis is performed to determine the roller parameters, wherein the roller parameters include path data, vibration state, and travel speed. There are various specific implementation manners for the parameter analysis, which are not limited in the embodiments of the present application. In one implementable manner, the path data is determined based on the compaction area size and the compaction area shape in the compaction area information; the vibration state is determined based on the compaction material type and the compaction material characteristics in the compaction area information; the travel speed is determined based on the compaction material type in the compaction area information and the device weight in the roller information; and the roller parameters are determined by comprehensively considering the path data, the vibration state, and the travel speed.

[0055] The unmanned roller in the related art can only realize the basic unmanned driving function, that is, the compaction operation is performed according to the fixed travel path and the fixed working parameters. However, in the actual compaction operation process, the different soil properties, the device performance differences of the compaction machine, and the complexity of the construction environment all affect the compaction quality after the roller works, that is, the compaction operation with the fixed working parameters cannot meet the requirement of the high compaction quality of the complex compaction area. Therefore, in order to solve the above technical defects, in the embodiments of the present application, the compaction area information and the roller information are comprehensively considered in the process of setting the roller parameters, so as to ensure that the roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area, so that the compaction effect can reach the expected effect based on the compaction area information, and at the same time, different roller devices can work under the optimal parameters to ensure that the compaction machine can fully exert its compaction effect. The comprehensive parameter analysis in the embodiments of the present application enables the roller to more accurately adapt to the compaction requirements of the current compaction area, reduces the fluctuation of the compaction quality, and improves the stability of the compaction quality.

[0056] Step S102: Real-time acquisition of spatial position and attitude information of the road roller, unmanned driving analysis based on the spatial position, attitude information and road roller parameters, generation of compaction control instructions, and control of the road roller for compaction operation by using the compaction control instructions.

[0057] For the embodiment of the present application, a high-precision GPS receiver and an attitude sensing sensor are arranged on the road roller. The high-precision GPS receiver can receive and process GPS signals in real time to obtain accurate latitude and longitude information of the road roller as the spatial position of the road roller. The attitude sensing sensor (for example, an angular displacement sensor, a gyroscope or the like) is used to measure the attitude information of the road roller in real time, including but not limited to pitch angle, yaw angle, roll angle and the like. Furthermore, the high-precision GPS receiver and the attitude sensing sensor are connected to the electronic device in a wireless manner, so that the electronic device can acquire the spatial position collected by the high-precision GPS receiver and the attitude information collected by the attitude sensing sensor in real time through wireless transmission.

[0058] Then, unmanned driving analysis is performed based on the spatial position, attitude information and road roller parameters, compaction control instructions are generated, and the road roller is controlled for compaction operation by using the compaction control instructions. The compaction control instructions are used to control the compaction machine to perform compaction operation according to the path data, vibration state and travel speed in the road roller parameters. The process of generating the compaction control instructions for unmanned driving analysis is as follows: based on the path data, spatial position and attitude information in the road roller parameters, the corresponding driving path of the road roller is generated, that is, the driving path is the walking path from the current state of the spatial position and attitude information to the end point in the path data; based on the vibration state and travel speed in the road roller parameters, the vibration frequency, amplitude, walking speed, turning ability and climbing ability of the road roller are determined, and the driving path, vibration frequency, amplitude, walking speed, turning ability and climbing ability are comprehensively used for instruction generation to obtain the compaction control instructions. The compaction control instructions are encoded in a format and protocol that can be understood by the road roller to ensure that the road roller can accurately and safely perform the compaction operation.

[0059] Step S103: In the process of performing the compaction operation by the road roller, multi-dimensional compaction data are acquired in real time, and the multi-dimensional compaction data are stored into a compaction operation database. The multi-dimensional compaction data include real-time driving parameters, real-time environmental sensing data, compaction operation parameters and device state information.

[0060] For the embodiment of the present application, in order to help the construction personnel to more accurately monitor the compaction operation process, the multi-dimensional compaction data are acquired and stored in real time, so as to understand the compaction effect of the road roller, thereby optimizing the subsequent compaction strategy, and at the same time, potential safety hazards can be found and warned in time, thereby enhancing the safety of construction.

[0061] Specifically, the real-time driving parameters are obtained through the high-precision GPS receiver and sensors on the road roller, i.e., the real-time position, speed, driving direction and other parameters are obtained through the high-precision GPS receiver of the road roller, and the acceleration, deceleration, steering angle and other dynamic driving parameters are obtained through the sensors of the road roller; of course, the actual running track of the road roller is also included. Real-time environmental perception data is obtained by using various sensors on the road roller, i.e., road and obstacle information such as road width, obstacle distance, road curvature, temperature, humidity, soil condition, etc. are captured in real time by using sensors such as laser radar and camera; the temperature of the asphalt mixture can also be measured in real time by using an infrared temperature sensor to monitor the temperature state of the pavement paving asphalt. The compaction operation parameters are obtained by using the pressure sensor and vibration sensor on the road roller, i.e., the vibration frequency, amplitude and other key parameters of the compaction operation are obtained according to the vibration sensor of the road roller; and the compaction degree, flatness, uniformity and other parameters are obtained by the pressure sensor. The device state information representing the working state of the road roller is obtained by using multiple types of sensors, such as engine speed, engine oil temperature, engine water temperature, engine oil pressure, hydraulic system pressure, hydraulic oil temperature, etc. Finally, the multi-dimensional compaction data is stored in the compaction operation database, and the compaction data corresponding to different compaction areas is stored in the compaction operation database, so as to facilitate the subsequent analysis of the compaction operation by the construction personnel, and to find out the problems and optimization points in the compaction process in time.

[0062] It can be seen that in the embodiment of the present application, the compaction area information and the road roller information are obtained, the parameter analysis is performed based on the compaction area information and the road roller information, and the road roller parameters are determined. Then, the spatial position and attitude information of the road roller are obtained in real time, the unmanned driving analysis is performed based on the spatial position, attitude information and road roller parameters, the compaction control instruction is generated, and the compaction control instruction is used to control the road roller to perform the compaction operation. Finally, in the process of the road roller performing the compaction operation, the multi-dimensional compaction data is obtained in real time, and the multi-dimensional compaction data is stored in the compaction operation database. In the process of setting the road roller parameters, the compaction area information and the road roller information are considered comprehensively to ensure that the road roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area, so that the compaction effect can reach the expected effect based on the compaction area information, so as to ensure that the compaction machine can fully play its compaction effect, reduce the fluctuation of the compaction quality, and improve the stability of the compaction quality.

[0063] Further, in order to ensure that the compaction effect of the subsequent compaction operation can reach the preset requirement and improve the overall compaction quality, in the embodiment of the present application, after the multi-dimensional compaction data is stored in the compaction operation database, it further includes:

[0064] extract the compaction operation whole-process data from the compaction operation database, wherein the compaction operation whole-process data comprises: a driving track corresponding to the road roller, land environment data corresponding to the driving track, and compaction evaluation indexes corresponding to the driving track;

[0065] based on the compaction evaluation indexes in the compaction operation whole-process data, divide the compaction area into compaction road sections, and determine the compaction road sections;

[0066] For the target compaction road section, based on the land environment data corresponding to the target compaction road section, the compaction evaluation indexes, and the road roller information, perform parameter optimization to determine optimized road roller parameters, wherein the target compaction road section is any one of the compaction road sections.

[0067] For the embodiments of the present application, the compaction operation is a key environment in road construction, and its compaction quality directly affects the durability and driving safety of the road. In the compaction operation, the working parameters of the road roller directly affect the compaction quality. Therefore, after the road roller completes a compaction operation, in-depth analysis is performed based on the compaction operation whole-process data to timely find potential quality problems, and the working parameters of the road roller are precisely adjusted to ensure that the compaction effect of subsequent compaction operations can meet the preset requirements, thereby improving the overall compaction quality.

[0068] Specifically, since the compaction operation database stores compaction data of each compaction operation corresponding to different compaction areas, the amount of stored data is huge and comprehensive. In order to facilitate the optimization of subsequent road roller operations for a compaction area, when it is detected that a compaction operation for the compaction area is completed, compaction operation whole-process data is extracted from the compaction operation database, wherein the compaction operation whole-process data comprises: a driving track corresponding to the road roller, land environment data corresponding to the driving track, and compaction evaluation indexes corresponding to the driving track. The compaction operation whole-process data is data information generated by the road roller performing the compaction operation in the compaction area. For the driving track corresponding to the road roller, it is used to represent the actual driving track of the road roller from the compaction starting point to the compaction termination point when performing the compaction operation. For the land environment data corresponding to the driving track, it is used to represent the land environment data collected by the road roller in the process of traveling along the driving track, including but not limited to: temperature, humidity, soil condition, etc. For the compaction evaluation indexes corresponding to the driving track, it is used to represent the compaction degree, flatness, uniformity, etc. of the road collected by the road roller in the process of traveling along the driving track.

[0069] Further, based on the compaction evaluation index in the compaction operation whole-process data, the driving track in the compaction area is divided into compaction road sections, and multiple compaction road sections are determined, that is, the electronic device pre-stores a corresponding relationship between the compaction evaluation index and the road division, the corresponding relationship divides the overall numerical range of the compaction evaluation index into multiple data size disjoint interval ranges, so that the road division of the compaction evaluation index in the same interval range is divided into the same compaction road section, and the numerical range in the corresponding relationship is the division of the single-dimensional index interval range. Then, for any target compaction road section in the multiple compaction road sections, based on the land environment data, the compaction evaluation index and the road roller information corresponding to the target compaction road section, the parameter optimization is performed to determine the optimized road roller parameters. The execution of the parameter optimization to obtain the optimized road roller parameters helps the road roller to achieve the best compaction effect in the next compaction operation, improves the density and strength of the road surface, and thus improves the overall quality of the road surface. The specific implementation process of the parameter optimization is as follows: according to the soil type and water content, the amplitude and frequency of the road roller are adjusted. Generally, viscous soil needs a larger amplitude and a lower frequency, while sandy soil may need a smaller amplitude and a higher frequency; according to the current compaction condition and the land environment in the compaction evaluation index, the driving speed of the road roller is adjusted, wherein a slower driving speed usually provides better compaction effect.

[0070] It can be seen that in the embodiment of the present application, when it is detected that one pass of compaction operation on the compaction area is completed, the compaction operation whole-process data is extracted from the compaction operation database, and based on the compaction evaluation index in the compaction operation whole-process data, the compaction road section division is performed on the compaction area to determine multiple compaction road sections. After the road roller completes one pass of compaction operation, in-depth analysis is performed based on the compaction operation whole-process data, so as to timely find potential quality problems, and through accurate adjustment of the working parameters of the road roller, it is ensured that the compaction effect of subsequent compaction operations can meet the preset requirements, thereby improving the overall compaction quality. Then, for the target compaction road section, based on the land environment data, the compaction evaluation index and the road roller information corresponding to the target compaction road section, the parameter optimization is performed to determine the optimized road roller parameters. The execution of the parameter optimization to obtain the optimized road roller parameters helps the road roller to achieve the best compaction effect in the next compaction operation, improves the density and strength of the road surface, and thus improves the overall quality of the road surface.

[0071] Further, in order to ensure the safety of the compaction operation and make the road roller more flexible to cope with various complex construction environments, in the embodiment of the present application, after the multi-dimensional compaction data is acquired in real time during the execution of the compaction operation by the road roller, the method further comprises:

[0072] obstacle identification based on the real-time environment perception data to determine obstacle information, wherein the obstacle information includes: obstacle type, obstacle position and obstacle size;

[0073] The current spatial position and current attitude information corresponding to the road roller are acquired, and autonomous obstacle avoidance planning is performed based on the current spatial position, the current attitude information, road roller parameters and obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes an obstacle avoidance type and an obstacle avoidance operation.

[0074] For the embodiments of the present application, the road roller may encounter various obstacles during operation, such as pedestrians, vehicles, road conical barrels and ground pits, which may all become safety hazards and pose a threat to the safety of the road roller. In order to ensure the safety of the compaction operation, obstacle identification and autonomous obstacle avoidance operations are performed during the execution of the compaction operation to significantly reduce the collision risk of the road roller during operation, so that the road roller can more flexibly cope with various complex construction environments.

[0075] Specifically, real-time environmental perception data is acquired using various sensors on the road roller, that is, road and obstacle information is captured in real time using sensors such as lidar and cameras, obstacle identification is performed based on the real-time environmental perception data, and obstacle information is determined, wherein the obstacle information includes obstacle type, obstacle position and obstacle size. For obstacle identification, based on the data collected by the camera and the lidar, image processing and point cloud processing are performed, the potential obstacle contour is extracted, and the features of the obstacle such as shape, size, color, texture, speed, etc. are extracted from the processed data, at the same time, machine learning or deep learning algorithms are used to classify and identify the extracted features to determine the type of the obstacle (e.g., person, vehicle, stone, tree, etc.); based on the real-time environmental perception data, the obstacle position is calculated to determine the absolute position of the obstacle and the relative position of the obstacle relative to the road roller, and according to the contour, distance information and possible shape model of the obstacle, the size of the obstacle (such as length, width, height) is calculated.

[0076] Further, the current spatial position and the current attitude information corresponding to the road roller are acquired, autonomous obstacle avoidance planning is performed based on the current spatial position, the current attitude information, the road roller parameters and the obstacle information, and autonomous obstacle avoidance information is determined, wherein the autonomous obstacle avoidance information includes an obstacle avoidance type and an obstacle avoidance operation. The specific implementation process of the autonomous obstacle avoidance planning is as follows: the relative distance and the relative position between the obstacle and the road roller are calculated according to the current spatial position and the current attitude information of the road roller and the position of the obstacle, so as to determine whether the obstacle is on the driving path of the road roller and whether the obstacle will pose a threat to the driving of the road roller. If the obstacle is on the driving path of the road roller, the driving distance between the obstacle and the road roller is calculated, and the distance threshold for trajectory change obstacle avoidance is acquired. The distance threshold can be the effective minimum distance for trajectory change obstacle avoidance. When the driving distance between the obstacle and the road roller is less than the distance threshold, it is determined that the obstacle avoidance type is emergency braking. Otherwise, it is determined that the obstacle avoidance type is trajectory change obstacle avoidance. Meanwhile, the obstacle avoidance operation is operation information corresponding to the obstacle avoidance type, so that the road roller can complete autonomous obstacle avoidance according to the obstacle avoidance operation, thereby ensuring the safety of the compaction operation. For example, when the obstacle avoidance type is trajectory change obstacle avoidance, the obstacle avoidance operation includes but is not limited to an obstacle avoidance path and obstacle avoidance driving information.

[0077] It can be seen that, in the embodiments of the present application, in order to ensure the safety of the compaction operation, obstacle identification is performed based on real-time environmental perception data, obstacle information is determined, then autonomous obstacle avoidance planning is performed based on the current spatial position, the current attitude information, the road roller parameters and the obstacle information of the road roller, and autonomous obstacle avoidance information is determined. Obstacle identification and autonomous obstacle avoidance operation are implemented, which significantly reduces the collision risk of the road roller during the operation process, so that the road roller can more flexibly cope with various complex construction environments.

[0078] Further, in order to improve the safety and stability of the road roller during the compaction operation process, optimize the work efficiency of the compaction operation, and reduce the risk of accidents, in the embodiments of the present application, after autonomous obstacle avoidance planning is performed based on the current spatial position, the current attitude information, the road roller parameters and the obstacle information, autonomous obstacle avoidance information is determined, and the following steps are further included:

[0079] When the obstacle avoidance type is trajectory change obstacle avoidance, obstacle avoidance control analysis is performed based on the obstacle avoidance path and the obstacle avoidance driving information in the obstacle avoidance operation, trajectory change obstacle avoidance instructions are generated, and the trajectory change obstacle avoidance instructions are sent to the road roller, so that the road roller avoids the obstacle during the compaction operation process;

[0080] When the obstacle avoidance type is emergency braking, the braking distance is calculated based on the road roller parameters, and the emergency braking distance is determined.

[0081] The pre-warning feasibility analysis is performed based on the current spatial position, the position of the obstacle and the emergency braking distance, and a pre-warning feasibility analysis result is determined.

[0082] When the pre-warning feasibility analysis result is pre-warning feasible, an emergency braking pre-warning is generated; when the pre-warning feasibility analysis result is pre-warning infeasible, the road roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so as to avoid obstacles by emergency braking during the compaction operation.

[0083] For the embodiments of the present application, precise and efficient obstacle avoidance control strategies are provided for obstacle avoidance requirements of different obstacle avoidance types, which not only improve the safety and stability of the road roller during the compaction operation, but also optimize the work efficiency of the compaction operation and reduce the risk of accidents.

[0084] Specifically, the obstacle avoidance path in the obstacle avoidance operation specifies an optimal path for avoiding obstacles from the current spatial position, and the obstacle avoidance driving information specifies the required steering angle, acceleration and speed parameters of the road roller driving on the obstacle avoidance path. Therefore, when the obstacle avoidance type is trajectory change obstacle avoidance, obstacle avoidance control analysis is performed based on the obstacle avoidance path and the obstacle avoidance driving information in the obstacle avoidance operation, and a trajectory change obstacle avoidance instruction is generated. The trajectory change obstacle avoidance instruction is encoded in a format and protocol that can be understood by the road roller. Then, the trajectory change obstacle avoidance instruction is sent to the road roller through a wireless communication network, so that the road roller avoids obstacles during the compaction operation.

[0085] When the obstacle avoidance type is emergency braking, an emergency braking pre-warning or an emergency braking operation can be selected, that is, when the distance between the road roller and the obstacle is much greater than the emergency braking distance, the emergency braking pre-warning can be preferentially selected to remind the operator to take emergency measures in time; when the distance between the road roller and the obstacle is close to the emergency braking distance, the emergency braking operation is directly selected to minimize the loss caused by improper obstacle avoidance. Therefore, the braking distance is calculated based on the parameters of the road roller to determine the emergency braking distance, and the formula for calculating the braking distance is as follows: S = v² / (2μg), where S represents the braking distance, v is the initial speed of the vehicle, g is the acceleration of gravity (about 9.8 m / s²), and μ is the friction coefficient between the tire and the ground, which is affected by many factors, such as road surface material, tire type, weather conditions, etc. Users can set the size of the friction coefficient according to actual needs.

[0086] Further, the pre-warning feasibility analysis is performed based on the current spatial position, the obstacle position and the emergency braking distance, and a pre-warning feasibility analysis result is determined, that is, the distance between two points is obtained by calculating the distance between the current spatial position and the obstacle position, and a pre-warning safety threshold is obtained, and if the difference between the distance between the two points and the emergency braking distance is greater than the pre-warning safety threshold, it indicates that the distance between the road roller and the obstacle is much greater than the emergency braking distance, and then the pre-warning feasibility analysis result is determined as pre-warning feasible; otherwise, the pre-warning feasibility analysis result is determined as pre-warning infeasible. Finally, when the pre-warning feasibility analysis result is pre-warning feasible, the emergency braking pre-warning is generated; and when the pre-warning feasibility analysis result is pre-warning infeasible, the road roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so as to avoid the obstacle by emergency braking during the compaction operation.

[0087] It can be seen that, in the embodiment of the present application, when the obstacle avoidance type is trajectory change obstacle avoidance, the obstacle avoidance control analysis is performed based on the obstacle avoidance path and the obstacle avoidance driving information in the obstacle avoidance operation, the trajectory change obstacle avoidance instruction is generated, and the trajectory change obstacle avoidance instruction is sent to the road roller, so that the road roller avoids the obstacle during the compaction operation. At the same time, when the obstacle avoidance type is emergency braking, the braking distance is calculated based on the road roller parameters, and the emergency braking distance is determined. Then, the pre-warning feasibility analysis is performed based on the current spatial position, the obstacle position and the emergency braking distance, and the pre-warning feasibility analysis result is determined. Further, when the pre-warning feasibility analysis result is pre-warning feasible, the emergency braking pre-warning is generated; and when the pre-warning feasibility analysis result is pre-warning infeasible, the road roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the road roller avoids the obstacle by emergency braking during the compaction operation. For different obstacle avoidance requirements of different obstacle avoidance types, accurate and efficient obstacle avoidance control strategies are provided, which not only improves the safety and stability of the road roller during the compaction operation, but also optimizes the working efficiency of the compaction operation and reduces the risk of accidents.

[0088] Further, in order to ensure that the road roller performs the compaction operation under the best conditions and improve the road compaction quality, in the embodiment of the present application, the parameter analysis is performed based on the compaction area information and the road roller information, and the road roller parameters are determined, including:

[0089] The path data is determined by performing the driving path analysis based on the compaction area size and the compaction area shape in the compaction area information;

[0090] The vibration state is determined by performing the vibration analysis based on the compaction material type and the compaction material characteristics in the compaction area information;

[0091] The travel speed is determined by performing the travel analysis based on the compaction material type in the compaction area information and the equipment weight in the road roller information;

[0092] The compactor parameters are determined based on the path data, vibration state and travel speed.

[0093] For the embodiments of the present application, in the actual compaction operation process, the different soil properties, the differences in the equipment performance of the compactor and the complexity of the construction environment will all affect the compaction quality after the compactor works, i.e., the type and characteristics of the compacted material will affect the compaction effect and the required parameters, and different compacted materials need different vibration states to fully exert their compaction effect. Therefore, in order to ensure that the compactor performs the compaction operation under the best conditions and improve the road compaction quality, the compaction area information and the compactor information are comprehensively considered to ensure that the compactor parameters can be highly matched with the type and characteristics of the compacted material in the compaction area, so that the compactor parameters based on the compaction area information can make the compaction effect reach the expected effect, and at the same time, different compactor equipment can work under the best parameters to ensure that the compactor can fully exert its compaction effect.

[0094] Specifically, the travel path is analyzed based on the size and shape of the compaction area in the compaction area information to determine the path data, i.e., based on the size and shape of the compaction area, the starting point and the end point of the compactor are determined, wherein the starting point and the end point are set at positions facilitating the entry and exit of the compactor and the operation; then, a travel path is automatically planned based on the starting point, the end point, the shape and size of the compaction area by using a path planning algorithm, and the travel path should be as smooth and continuous as possible to avoid sharp turns and frequent stops, and the finally determined travel path is recorded as the path data.

[0095] Further, the vibration analysis is performed based on the type and characteristics of the compacted material in the compaction area information to determine the vibration state, i.e., different compacted material types are suitable for different vibration modes, for example, for hard materials such as sand and gravel, a high-frequency low-amplitude vibration mode is suitable; and for soft materials such as clay and asphalt, a low-frequency high-amplitude vibration mode is more suitable; on this basis, the specific vibration frequency and amplitude are determined by using the corresponding relationship between the vibration parameters and the characteristics of the compacted material and the characteristics of the compacted material, and are recorded as the vibration state, wherein the above-mentioned corresponding relationship defines the corresponding relationship between different material characteristics and vibration parameters, for example, the corresponding relationship between the water content value and the vibration parameters, and the corresponding relationship between the particle size and the vibration parameters.

[0096] Meanwhile, for the same type of road roller, the compaction effect of the road roller is different for different compaction material types; for the same type of compaction material, the compaction effect of different road rollers is also different for the same type of compaction material; of course, the driving speed of the road roller also affects the compaction ability and compaction effect of the road roller. Therefore, in order to ensure that the road roller performs the compaction operation under the best condition and improve the road compaction quality, the driving speed is determined based on the compaction material type in the compaction area information and the equipment weight in the road roller information. The corresponding relationship between the compaction material type, the equipment weight of the road roller and the driving speed is pre-set in the electronic device under the premise of standard compaction effect, so that the driving speed corresponding to the road roller can be quickly determined based on the above corresponding relationship. Finally, the path data, the vibration state and the driving speed are comprehensively determined to determine the road roller parameters.

[0097] It can be seen that, in the embodiment of the present application, in order to ensure that the road roller performs the compaction operation under the best condition and improve the road compaction quality, the compaction area information and the road roller information are comprehensively considered in the process of performing the parameter analysis. Therefore, the path data is determined based on the compaction area size and the compaction area shape in the compaction area information. Further, the vibration state is determined based on the compaction material type and the compaction material characteristics in the compaction area information. Meanwhile, the driving speed is determined based on the compaction material type in the compaction area information and the equipment weight in the road roller information. Finally, the path data, the vibration state and the driving speed are comprehensively determined to determine the road roller parameters. The compaction area information and the road roller information are comprehensively considered to ensure that the road roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area.

[0098] Further, in order to enable the construction personnel to clearly see the compaction operation effect and facilitate timely discovery of problems in the compaction operation, in the embodiment of the present application, after the multi-dimensional compaction data is stored in the compaction operation database, the method further includes:

[0099] The target compaction operation data set is determined based on the compaction operation database, wherein the target compaction operation data set includes: multi-pass road compaction data, and each pass of road compaction data is multi-dimensional compaction data corresponding to the same compaction area.

[0100] An engineering construction map is obtained, and the compaction visualization display is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization display map, wherein the compaction visualization display map displays each pass of road compaction data in a separate layer.

[0101] For the embodiments of the present application, the compaction data is often in an abstract form. In order to convert the abstract compaction data into intuitive images or graphics, so that the construction personnel can see the effect of the compaction operation at a glance, a compaction visualization display method is adopted, each round of road compaction data is displayed in the form of a visual layer, which helps the construction personnel to accurately judge whether the compaction operation meets the design requirements, and is conducive to timely discovering problems in the compaction operation, so as to ensure the quality and progress of the compaction operation.

[0102] Specifically, the compaction region information to be filtered is input into the compaction operation database, the compaction passes of the compaction region are queried in the compaction operation database, and the operation data filtering is performed in the compaction operation database based on the compaction region information and the compaction passes, to determine a target compaction operation data set, wherein the target compaction operation data set includes: multiple rounds of road compaction data, and each round of road compaction data is multi-dimensional compaction data corresponding to the same compaction region. Then, an engineering construction map is obtained, which can be obtained from a public database or a professional map service website, and the engineering construction map is format-converted to be compatible with subsequent visualization tools. Further, multiple layers are created in the visualization tool, each layer corresponds to one round of road compaction data, and different colors, icons or line styles are set for the layers according to different data items in each round of road compaction data, to distinguish different compaction effects. Of course, the transparency, color gradient and other parameters of the layers can also be adjusted to make the map more beautiful and easy to understand, and interactive elements such as legends, annotations or floating windows are added, so that the user can more conveniently obtain specific information of the compaction operation. Finally, a compaction visualization display map is obtained, which can realize functions such as real-time viewing of compaction data and user interaction.

[0103] As can be seen, in the embodiments of the present application, in order to convert the abstract compaction data into intuitive images or graphics, so that the construction personnel can see the effect of the compaction operation at a glance. Therefore, the operation data filtering is performed based on the compaction operation database to determine a target compaction operation data set, and then the compaction visualization display is performed based on the engineering construction map and the target compaction operation data set to obtain a compaction visualization display map. The compaction visualization display helps the construction personnel to accurately judge whether the compaction operation meets the design requirements, and is conducive to timely discovering problems in the compaction operation, so as to ensure the quality and progress of the compaction operation.

[0104] The above embodiment introduces an unmanned intelligent compaction method from the perspective of method flow, and the following embodiment introduces an unmanned intelligent compaction system from the perspective of virtual modules or virtual units. For details, see the following embodiments.

[0105] The embodiments of the present application provide an unmanned intelligent compaction system, as shown in Fig. 2As shown, the unmanned intelligent compaction system can specifically include:

[0106] The parameter analysis module 210 is configured to acquire compaction area information and roller information, perform parameter analysis based on the compaction area information and the roller information, and determine roller parameters, wherein the roller parameters include path data, vibration state, and travel speed.

[0107] The control instruction generation module 220 is configured to acquire spatial position and attitude information of the roller in real time, perform unmanned analysis based on the spatial position, attitude information, and roller parameters, generate a compaction control instruction, and control the roller to perform compaction work by using the compaction control instruction.

[0108] The compaction data storage module 230 is configured to acquire multi-dimensional compaction data in real time during execution of the compaction work by the roller, and store the multi-dimensional compaction data to a compaction work database, wherein the multi-dimensional compaction data includes real-time travel parameters, real-time environmental perception data, compaction work parameters, and equipment state information.

[0109] For the embodiments of the present application, the compaction area information and the roller information are acquired, parameter analysis is performed based on the compaction area information and the roller information, and roller parameters are determined. Then, the spatial position and attitude information of the roller are acquired in real time, unmanned analysis is performed based on the spatial position, attitude information, and roller parameters, a compaction control instruction is generated, and the roller is controlled to perform compaction work by using the compaction control instruction. Finally, multi-dimensional compaction data is acquired in real time during execution of the compaction work by the roller, and the multi-dimensional compaction data is stored to a compaction work database. During setting of the roller parameters, the compaction area information and the roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area, so that the compaction effect can reach the expected effect on the basis of the compaction area information, to ensure that the compactor can fully exert its compaction effect, reduce the fluctuation of the compaction quality, and improve the stability of the compaction quality.

[0110] In one possible implementation of the embodiments of the present application, the unmanned intelligent compaction system further includes:

[0111] The parameter optimization module is configured to extract compaction work whole-process data from the compaction work database when it is detected that one round of compaction work on the compaction area is completed, wherein the compaction work whole-process data includes a travel track corresponding to the roller, land environmental data corresponding to the travel track, and compaction evaluation indexes corresponding to the travel track.

[0112] The compaction area is divided into multiple compaction road segments based on the compaction evaluation indexes in the compaction work whole-process data.

[0113] For the target compaction section, the target compaction section is any one of the multiple compaction sections.

[0114] In a possible implementation of the embodiment of the application, the unmanned intelligent compaction system further includes:

[0115] The autonomous obstacle avoidance planning module is configured to identify obstacles based on the real-time environment perception data and determine obstacle information, wherein the obstacle information includes an obstacle type, an obstacle position, and an obstacle size.

[0116] The current spatial position and the current attitude information of the road roller are obtained, and autonomous obstacle avoidance planning is performed based on the current spatial position, the current attitude information, the road roller parameters, and the obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information includes an obstacle avoidance type and an obstacle avoidance operation.

[0117] In a possible implementation of the embodiment of the application, the unmanned intelligent compaction system further includes:

[0118] The autonomous obstacle avoidance execution module is configured to, when the obstacle avoidance type is trajectory change obstacle avoidance, perform obstacle avoidance control analysis based on an obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation, generate a trajectory change obstacle avoidance instruction, and send the trajectory change obstacle avoidance instruction to the road roller, so that the road roller avoids the obstacle during the compaction operation.

[0119] When the obstacle avoidance type is emergency braking, a braking distance is calculated based on the road roller parameters to determine an emergency braking distance.

[0120] A warning feasibility analysis is performed based on the current spatial position, the obstacle position, and the emergency braking distance to determine a warning feasibility analysis result.

[0121] When the warning feasibility analysis result is that the warning is feasible, an emergency braking warning is generated; and when the warning feasibility analysis result is that the warning is not feasible, the road roller is controlled to perform the emergency braking operation in the obstacle avoidance operation, so that the road roller avoids the obstacle by emergency braking during the compaction operation.

[0122] In a possible implementation of the embodiment of the application, when the parameter analysis module 210 performs the parameter analysis based on the compaction area information and the road roller information to determine the road roller parameters, the parameter analysis module 210 is configured to:

[0123] A driving path is analyzed based on a compaction area size and a compaction area shape in the compaction area information to determine path data.

[0124] Vibration is analyzed based on a compaction material type and a compaction material characteristic in the compaction area information to determine a vibration state.

[0125] based on the compaction material type in the compaction area information and the equipment weight in the roller information, performing travel analysis to determine a travel speed;

[0126] based on the path data, the vibration state and the travel speed, determining a roller parameter.

[0127] In a possible implementation of the embodiment of the application, the unmanned intelligent compaction system further includes:

[0128] The visualization display module is configured to perform job data filtering based on the compaction job database to determine a target compaction job data set, wherein the target compaction job data set includes: multiple-pass road compaction data, each pass of road compaction data being multi-dimensional compaction data corresponding to a same compaction area.

[0129] obtain an engineering construction map, perform compaction visualization display based on the engineering construction map and the target compaction job data set, and obtain a compaction visualization display map, wherein the compaction visualization display map displays each pass of road compaction data in a separate layer.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described unmanned intelligent compaction system can refer to the corresponding process in the foregoing method embodiments, which will not be described herein.

[0131] In the embodiment of the application, an electronic device is provided, as shown in Fig. 3 The electronic device 300 shown in Fig. 3 The electronic device 300 shown in

[0132] The processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in connection with the disclosure. The processor 301 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0133] The bus 302 can include a path for transmitting information between the above-mentioned components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Fig. 3 In the figure, only one thick line is used, but it does not mean that there is only one bus or one type of bus.

[0134] The memory 303 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, an optical disk storage (including a compact disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto.

[0135] The memory 303 is configured to store application program codes for implementing the solutions of the present application, and the processor 301 is configured to control the execution of the application program codes.

[0136] The electronic device includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a car terminal (for example, a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. It can also be a server or the like. Fig. 3 The electronic device shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0137] The embodiments of the present application provide a computer readable storage medium, which stores a computer program. When the computer program is run on a computer, the computer can execute the corresponding content in the foregoing method embodiments.

[0138] The embodiments of the present application provide a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented. Compared with the related art, the embodiments of the present application acquire compaction area information and roller information, perform parameter analysis based on the compaction area information and the roller information, and determine roller parameters. Then, the spatial position and attitude information of the roller are acquired in real time, unmanned driving analysis is performed based on the spatial position, the attitude information and the roller parameters, a compaction control instruction is generated, and the roller is controlled to perform compaction work by using the compaction control instruction. Finally, in the process of performing the compaction work by the roller, multi-dimensional compaction data are acquired in real time and stored in a compaction work database. In the process of setting the roller parameters, the compaction area information and the roller information are comprehensively considered to ensure that the roller parameters can be highly matched with the compaction material type and the compaction material characteristics of the compaction area, so that the compaction effect can reach the expected effect based on the compaction area information, the compaction machine can fully exert its compaction effect, the fluctuation of the compaction quality is reduced, and the stability of the compaction quality is improved.

[0139] It should be understood that although the steps in the flowcharts of the drawings are shown in a sequential order following the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated otherwise herein, the execution of the steps is not strictly limited to the order indicated by the arrows, and can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of which is not necessarily sequential, but can be round-robin or alternating with at least some of the other steps or sub-steps or stages of other steps.

[0140] The above is only some embodiments of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should also be considered as the protection scope of the present application.

Claims

1. A method of unmanned intelligent compaction, characterized in that, The method comprises the following steps: acquiring compaction area information and roller information, performing parameter analysis based on the compaction area information and the roller information, and determining roller parameters, wherein the roller parameters include path data, vibration state, and travel speed; acquiring spatial position and attitude information of the roller in real time, performing unmanned analysis based on the spatial position, the attitude information, and the roller parameters, generating compaction control instructions, and controlling the roller to perform compaction work by using the compaction control instructions, wherein the attitude information includes pitch angle, yaw angle, and roll angle; acquiring multi-dimensional compaction data in real time during the compaction work performed by the roller, and storing the multi-dimensional compaction data in a compaction work database, wherein the multi-dimensional compaction data includes real-time travel parameters, real-time environmental perception data, compaction work parameters, and equipment state information; the parameter analysis based on the compaction area information and the roller information to determine the roller parameters comprises: performing travel path analysis based on the compaction area size and the compaction area shape in the compaction area information to determine the path data; performing vibration analysis based on the compaction material type and the compaction material characteristics in the compaction area information to determine the vibration state; performing travel analysis based on the compaction material type in the compaction area information and the equipment weight in the roller information to determine the travel speed; determining the roller parameters by comprehensively considering the path data, the vibration state, and the travel speed; wherein the compaction material type includes hard material and soft material, and the compaction material characteristics include water content value and particle size; the vibration analysis based on the compaction material type and the compaction material characteristics in the compaction area information to determine the vibration state comprises: determining a high-frequency low-amplitude vibration mode when the compaction material type is the hard material; determining a low-frequency high-amplitude vibration mode when the compaction material type is the soft material; retrieving a corresponding relationship between material characteristics and vibration parameters, determining vibration parameters based on the compaction material characteristics and the corresponding relationship; the vibration mode and the vibration parameters constitute the vibration state, and the vibration parameters include vibration frequency and amplitude; after storing the multi-dimensional compaction data in the compaction work database, the method further comprises: extracting compaction work whole-process data from the compaction work database when it is detected that one round of compaction work on the compaction area is completed, wherein the compaction work whole-process data includes the travel trajectory corresponding to the roller, the land environment data corresponding to the travel trajectory, and the compaction evaluation indexes corresponding to the travel trajectory, and the compaction evaluation indexes include compaction degree, flatness, and uniformity; performing compaction section division on the compaction area based on the compaction evaluation indexes in the compaction work whole-process data to determine multi-section compaction sections; for a target compaction section, performing parameter optimization based on the land environment data, the compaction evaluation indexes, and the roller information corresponding to the target compaction section to determine optimized roller parameters, wherein the target compaction section is any one of the multi-section compaction sections.

2. The unmanned intelligent compaction method of claim 1, wherein, The method further comprises the following steps after the multi-dimensional compaction data is acquired in real time during the compactor performs the compaction operation: obstacle information is determined based on the real-time environmental perception data, wherein the obstacle information comprises an obstacle type, an obstacle position, and an obstacle size; current spatial position and current attitude information of the compactor are acquired, and autonomous obstacle avoidance planning is performed based on the current spatial position, the current attitude information, the compactor parameters, and the obstacle information to determine autonomous obstacle avoidance information, wherein the autonomous obstacle avoidance information comprises an obstacle avoidance type and an obstacle avoidance operation.

3. The unmanned intelligent compaction method of claim 2, wherein, The method further comprises the following steps after the autonomous obstacle avoidance information is determined based on the current spatial position, the current attitude information, the compactor parameters, and the obstacle information: when the obstacle avoidance type is trajectory change obstacle avoidance, obstacle avoidance control analysis is performed based on the obstacle avoidance path and obstacle avoidance driving information in the obstacle avoidance operation to generate a trajectory change obstacle avoidance instruction, and the trajectory change obstacle avoidance instruction is sent to the compactor to enable the compactor to avoid the obstacle during the compaction operation; when the obstacle avoidance type is emergency braking, a braking distance is calculated based on the compactor parameters to determine an emergency braking distance; warning feasibility analysis is performed based on the current spatial position, the obstacle position, and the emergency braking distance to determine a warning feasibility analysis result; when the warning feasibility analysis result is that the warning is feasible, an emergency braking warning is generated; and when the warning feasibility analysis result is that the warning is not feasible, the compactor is controlled to perform the emergency braking operation in the obstacle avoidance operation to enable the compactor to avoid the obstacle during the compaction operation.

4. The unmanned intelligent compaction method of claim 1, wherein, The method further comprises the following steps after the multi-dimensional compaction data is stored in the compaction operation database: target compaction operation data sets are determined based on operation data screening of the compaction operation database, wherein the target compaction operation data sets comprise multi-pass road compaction data, and each pass of the road compaction data is multi-dimensional compaction data corresponding to a same compaction area; an engineering construction map is acquired, and a compaction visual display map is obtained based on compaction visual display of the engineering construction map and the target compaction operation data sets, wherein the compaction visual display map displays each pass of the road compaction data in a separate layer.

5. An electronic device, comprising: The method comprises: at least one processor; a memory; at least one application program, wherein the at least one application program is stored in the memory and configured to be executed by the at least one processor, and the at least one application program is configured to execute the unmanned intelligent compaction method of any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the unmanned intelligent compaction method of any one of claims 1 to 4.

7. A computer program product, characterised in that, A computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the unmanned intelligent compaction method of any one of claims 1 to 4.

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